


from typing import List, Optional, Union

from ...image_utils import ImageInput
from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType







class 							lowerCamelCase__						(				UpperCAmelCase   ):






			"""simple docstring"""






			_UpperCamelCase		:       Tuple 			=		['image_processor', 'tokenizer']
			_UpperCamelCase		:       Dict 			=		'BlipImageProcessor'
			_UpperCamelCase		:       Tuple 			=		('BertTokenizer', 'BertTokenizerFast')




			def __init__(				self   ,    snake_case   ,    snake_case  ):

						'''simple docstring'''


						UpperCamelCase__       =							False
						super().__init__(snake_case   ,    snake_case  )
						UpperCamelCase__       =							self.image_processor




			def __call__(				self   ,    snake_case = None   ,    snake_case = None   ,    snake_case = True   ,    snake_case = False   ,    snake_case = None   ,    snake_case = None   ,    snake_case = 0   ,    snake_case = None   ,    snake_case = None   ,    snake_case = False   ,    snake_case = False   ,    snake_case = False   ,    snake_case = False   ,    snake_case = False   ,    snake_case = True   ,    snake_case = None   ,    **snake_case   ,    ):

						'''simple docstring'''


						if images is None and text is None:
									raise ValueError("You have to specify either images or text."  )

						# Get only text
						if images is None:
									UpperCamelCase__       =							self.tokenizer
									UpperCamelCase__       =							self.tokenizer(
									    text=snake_case   ,    add_special_tokens=snake_case   ,    padding=snake_case   ,    truncation=snake_case   ,    max_length=snake_case   ,    stride=snake_case   ,    pad_to_multiple_of=snake_case   ,    return_attention_mask=snake_case   ,    return_overflowing_tokens=snake_case   ,    return_special_tokens_mask=snake_case   ,    return_offsets_mapping=snake_case   ,    return_token_type_ids=snake_case   ,    return_length=snake_case   ,    verbose=snake_case   ,    return_tensors=snake_case   ,    **snake_case   ,    )
									return text_encoding

						# add pixel_values
						UpperCamelCase__       =							self.image_processor(snake_case   ,    return_tensors=snake_case  )

						if text is not None:
									UpperCamelCase__       =							self.tokenizer(
									    text=snake_case   ,    add_special_tokens=snake_case   ,    padding=snake_case   ,    truncation=snake_case   ,    max_length=snake_case   ,    stride=snake_case   ,    pad_to_multiple_of=snake_case   ,    return_attention_mask=snake_case   ,    return_overflowing_tokens=snake_case   ,    return_special_tokens_mask=snake_case   ,    return_offsets_mapping=snake_case   ,    return_token_type_ids=snake_case   ,    return_length=snake_case   ,    verbose=snake_case   ,    return_tensors=snake_case   ,    **snake_case   ,    )
						else:
									UpperCamelCase__       =							None

						if text_encoding is not None:
									encoding_image_processor.update(snake_case  )

						return encoding_image_processor




			def 					snake_case__					(				self   ,    *snake_case   ,    **snake_case  ):

						'''simple docstring'''


						return self.tokenizer.batch_decode(*snake_case   ,    **snake_case  )




			def 					snake_case__					(				self   ,    *snake_case   ,    **snake_case  ):

						'''simple docstring'''


						return self.tokenizer.decode(*snake_case   ,    **snake_case  )







			@property
			def 					snake_case__					(				self  ):

						'''simple docstring'''


						UpperCamelCase__       =							self.tokenizer.model_input_names
						UpperCamelCase__       =							self.image_processor.model_input_names
						return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names  )  )






from __future__ import annotations

import numpy as np
def 							UpperCamelCase_(					_A :list[float]	)->		Optional[Any]:
			return np.maximum(0					,  _A	)


if __name__ == "__main__":
						print(np.array(relu([-1, 0, 5])))  # --> [0, 0, 5]


